Adaptive routing to avoid non-repairable memory and logic defects on automata processor

ABSTRACT

Systems and methods for utilizing a defect map to configure an automata processor in order to avoid defects when configuring the automata processor. A system includes automata processor having a state machine lattice. The system also includes a non-volatile memory having a defect map stored thereon and indicating logical defects found on the automata processor. By including the defect map, a compiler may access the defect map to map out defects in the automata processor during configuring to avoid such defects.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.15/216,507, entitled “Adaptive routing to avoid non-repairable memoryand logic defects on automata processor,” and filed Jul. 21, 2016, theentirety of which is incorporated by reference herein for all purposes.

BACKGROUND Field of Invention

Embodiments of the invention relate generally to electronic deviceshaving automata processors and, more specifically, in certainembodiments, to failure mapping of automata processors.

Description of Related Art

Certain computational electronic devices and systems may include anumber of processing resources (e.g., one or more processors), which mayretrieve and execute instructions and store the results of the executedinstructions to a suitable location. For example, the processingresources may include a number of functional units, arithmetic units,and similar circuitry to execute instructions by performing a number ofBoolean logical operations and arithmetic functions. One particularprocessing resource may include an automata processor, which may besuitable for use in applications such as, for example, network security,computational biology, image processing, text searching, and so forth.These automata processors, may include, a combination of logicalelements and dynamic random access memory (DRAM) cells which may belinked together and programmed in a multitude of ways to carry out adesirable function, such as pattern recognition. By configuring thedifferent elements, and the connections between them, a programmer cancreate thousands of state machines on each chip all interconnected by aprogrammable fabric. These state machines can perform regular expressionsearches at performance levels that far exceed conventional approachesthat may be limited by inherent inefficiencies found in systemsutilizing a von Newmann architecture.

However, unlike typical DRAM memory arrays that utilize a von Newmannarchitecture, the automata processor may have large critical areas ofthe array that may only be minimally repairable. Based on the nature anddesign of the automata processor, a single flaw in the logical elementsor DRAM cells may render an entire automata processor chip useless.Because defects in automata processors are difficult to repair or simplyreplace with redundant elements, minor flaws in the chips may result inthe scrapping of the entire automata processor chip. It would be usefulto provide a mechanism for utilizing automata processor chips havingsome acceptable threshold of minor defects in order to increase useableyield.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example of system having a state machine engine,according to various embodiments.

FIG. 2 illustrates an example of an FSM lattice of the state machineengine of FIG. 1, according to various embodiments.

FIG. 3 illustrates an example of a block of the FSM lattice of FIG. 2,according to various embodiments.

FIG. 4 illustrates an example of a row of the block of FIG. 3, accordingto various embodiments.

FIG. 4A illustrates a block as in FIG. 3 having counters in rows of theblock, according to various embodiments.

FIG. 5 illustrates an example of a Group of Two of the row of FIG. 4,according to embodiments.

FIG. 6 illustrates an example of a finite state machine graph, accordingto various embodiments.

FIG. 7 illustrates an example of two-level hierarchy implemented withFSM lattices, according to various embodiments.

FIG. 7A illustrates a second example of two-level hierarchy implementedwith FSM lattices, according to various embodiments.

FIG. 8 illustrates an example of a method for a compiler to convertsource code into a binary file for programming of the FSM lattice ofFIG. 2, according to various embodiments.

FIG. 9 illustrates a state machine engine, according to variousembodiments.

FIG. 10 illustrates a card having automata processors and non-volatilememory for storing defect data, according to various embodiments.

FIG. 11 is a flow chart illustrating a method of creating and utilizingdefect data to selectively program one or more processors, according tovarious embodiments.

DETAILED DESCRIPTION

Turning now to the figures, FIG. 1 illustrates an embodiment of aprocessor-based system, generally designated by reference numeral 10.The system 10 may be any of a variety of types such as a desktopcomputer, laptop computer, pager, cellular phone, personal organizer,portable audio player, control circuit, camera, etc. The system 10 mayalso be a network node, such as a router, a server, or a client (e.g.,one of the previously-described types of computers). The system 10 maybe some other sort of electronic device, such as a copier, a scanner, aprinter, a game console, a television, a set-top video distribution orrecording system, a cable box, a personal digital media player, afactory automation system, an automotive computer system, or a medicaldevice. (The terms used to describe these various examples of systems,like many of the other terms used herein, may share some referents and,as such, should not be construed narrowly in virtue of the other itemslisted.)

In a typical processor-based device, such as the system 10, a processor12, such as a microprocessor, controls the processing of systemfunctions and requests in the system 10. Further, the processor 12 maycomprise a plurality of processors that share system control. Theprocessor 12 may be coupled directly or indirectly to each of theelements in the system 10, such that the processor 12 controls thesystem 10 by executing instructions that may be stored within the system10 or external to the system 10.

In accordance with the embodiments described herein, the system 10includes a state machine engine or automata processor 14, which mayoperate under control of the processor 12. As used herein, the terms“state machine engine” and “automata processor” are usedinterchangeably. The state machine engine 14 may employ any one of anumber of state machine architectures, including, but not limited toMealy architectures, Moore architectures, Finite State Machines (FSMs),Deterministic FSMs (DFSMs), Bit-Parallel State Machines (BPSMs), etc.Though a variety of architectures may be used, for discussion purposes,the application refers to FSMs. However, those skilled in the art willappreciate that the described techniques may be employed using any oneof a variety of state machine architectures. Also, while a single statemachine engine or automata processor 14 is illustrated in FIG. 1, itwill be appreciated that the system 10 may include multiple statemachine engines 14, as described further below with respect to FIG. 10.

As discussed further below, the state machine engine 14 may include anumber of (e.g., one or more) finite state machine (FSM) lattices (e.g.,core of a chip). For purposes of this application the term “lattice”refers to an organized framework (e.g., routing matrix, routing network,frame) of elements (e.g., Boolean cells, counter cells, state machineelements, state transition elements). Furthermore, the “lattice” mayhave any suitable shape, structure, or hierarchical organization (e.g.,grid, cube, spherical, cascading). Each FSM lattice may implementmultiple FSMs that each receive and analyze the same data in parallel.Further, the FSM lattices may be arranged in groups (e.g., clusters),such that clusters of FSM lattices may analyze the same input data inparallel. Further, clusters of FSM lattices of the state machine engine14 may be arranged in a hierarchical structure wherein outputs fromstate machine lattices on a lower level of the hierarchical structuremay be used as inputs to state machine lattices on a higher level. Bycascading clusters of parallel FSM lattices of the state machine engine14 in series through the hierarchical structure, increasingly complexpatterns may be analyzed (e.g., evaluated, searched, etc.).

Further, based on the hierarchical parallel configuration of the statemachine engine 14, the state machine engine 14 can be employed forcomplex data analysis (e.g., pattern recognition or other processing) insystems that utilize high processing speeds. For instance, embodimentsdescribed herein may be incorporated in systems with processing speedsof 1 GByte/sec. Accordingly, utilizing the state machine engine 14, datafrom high speed memory devices or other external devices may be rapidlyanalyzed. The state machine engine 14 may analyze a data streamaccording to several criteria (e.g., search terms), at about the sametime, e.g., during a single device cycle. Each of the FSM latticeswithin a cluster of FSMs on a level of the state machine engine 14 mayeach receive the same search term from the data stream at about the sametime, and each of the parallel FSM lattices may determine whether theterm advances the state machine engine 14 to the next state in theprocessing criterion. The state machine engine 14 may analyze termsaccording to a relatively large number of criteria, e.g., more than 100,more than 110, or more than 10,000. Because they operate in parallel,they may apply the criteria to a data stream having a relatively highbandwidth, e.g., a data stream of greater than or generally equal to 1GByte/sec, without slowing the data stream.

In one embodiment, the state machine engine 14 may be configured torecognize (e.g., detect) a great number of patterns in a data stream.For instance, the state machine engine 14 may be utilized to detect apattern in one or more of a variety of types of data streams that a useror other entity might wish to analyze. For example, the state machineengine 14 may be configured to analyze a stream of data received over anetwork, such as packets received over the Internet or voice or datareceived over a cellular network. In one example, the state machineengine 14 may be configured to analyze a data stream for spam ormalware. The data stream may be received as a serial data stream, inwhich the data is received in an order that has meaning, such as in atemporally, lexically, or semantically significant order. Alternatively,the data stream may be received in parallel or out of order and, then,converted into a serial data stream, e.g., by reordering packetsreceived over the Internet. In some embodiments, the data stream maypresent terms serially, but the bits expressing each of the terms may bereceived in parallel. The data stream may be received from a sourceexternal to the system 10, or may be formed by interrogating a memorydevice, such as the memory 16, and forming the data stream from datastored in the memory 16. In other examples, the state machine engine 14may be configured to recognize a sequence of characters that spell acertain word, a sequence of genetic base pairs that specify a gene, asequence of bits in a picture or video file that form a portion of animage, a sequence of bits in an executable file that form a part of aprogram, or a sequence of bits in an audio file that form a part of asong or a spoken phrase. The stream of data to be analyzed may includemultiple bits of data in a binary format or other formats, e.g., baseten, ASCII, etc. The stream may encode the data with a single digit ormultiple digits, e.g., several binary digits.

As will be appreciated, the system 10 may include memory 16. The memory16 may include volatile memory, such as Dynamic Random Access Memory(DRAM), Static Random Access Memory (SRAM), Synchronous DRAM (SDRAM),Double Data Rate DRAM (DDR SDRAM), DDR2 SDRAM, DDR3 SDRAM, DDR4 SDRAMetc. The memory 16 may also include non-volatile memory, such asread-only memory (ROM), PC-RAM, silicon-oxide-nitride-oxide-silicon(SONOS) memory, metal-oxide-nitride-oxide-silicon (MONOS) memory,polysilicon floating gate based memory, and/or other types of flashmemory of various architectures (e.g., NAND memory, NOR memory, etc.) tobe used in conjunction with the volatile memory. The memory 16 mayinclude one or more memory devices, such as DRAM devices, that mayprovide data to be analyzed by the state machine engine 14. As usedherein, the term “provide” may generically refer to direct, input,insert, issue, route, send, transfer, transmit, generate, give, makeavailable, move, output, pass, place, read out, write, etc. Such devicesmay be referred to as or include solid state drives (SSD's),MultimediaMediaCards (MMC's), SecureDigital (SD) cards, CompactFlash(CF) cards, or any other suitable device. Further, it should beappreciated that such devices may couple to the system 10 via anysuitable interface, such as Universal Serial Bus (USB), PeripheralComponent Interconnect (PCI), PCI Express (PCI-E), Small Computer SystemInterface (SCSI), IEEE 1394 (Firewire), or any other suitable interface.To facilitate operation of the memory 16, such as the flash memorydevices, the system 10 may include a memory controller (notillustrated). As will be appreciated, the memory controller may be anindependent device or it may be integral with the processor 12.Additionally, the system 10 may include an external storage 18, such asa magnetic storage device. The external storage may also provide inputdata to the state machine engine 14.

The system 10 may include a number of additional elements. For instance,a compiler 20 may be used to configure (e.g., program) the state machineengine 14, as described in more detail with regard to FIG. 8. An inputdevice 22 may also be coupled to the processor 12 to allow a user toinput data into the system 10. For instance, an input device 22 may beused to input data into the memory 16 for later analysis by the statemachine engine 14. The input device 22 may include buttons, switchingelements, a keyboard, a light pen, a stylus, a mouse, and/or a voicerecognition system, for instance. An output device 24, such as a displaymay also be coupled to the processor 12. The display 24 may include anLCD, a CRT, LEDs, and/or an audio display, for example. They system mayalso include a network interface device 26, such as a Network InterfaceCard (NIC), for interfacing with a network, such as the Internet. Aswill be appreciated, the system 10 may include many other components,depending on the application of the system 10.

FIGS. 2-5 illustrate an example of a FSM lattice 30. In an example, theFSM lattice 30 comprises an array of blocks 32. As will be described,each block 32 may include a plurality of selectively couple-ablehardware elements (e.g., configurable elements and/or special purposeelements) that correspond to a plurality of states in a FSM. Similar toa state in a FSM, a hardware element can analyze an input stream andactivate a downstream hardware element, based on the input stream.

The configurable elements can be configured (e.g., programmed) toimplement many different functions. For instance, the configurableelements may include state transition elements (STEs) 34, 36 (shown inFIG. 5) that function as data analysis elements and are hierarchicallyorganized into rows 38 (shown in FIGS. 3 and 4) and blocks 32 (shown inFIGS. 2 and 3). The STEs each may be considered an automaton, e.g., amachine or control mechanism designed to follow automatically apredetermined sequence of operations or respond to encoded instructions.Taken together, the STEs form an automata processor as state machineengine 14. To route signals between the hierarchically organized STEs34, 36, a hierarchy of configurable switching elements can be used,including inter-block switching elements 40 (shown in FIGS. 2 and 3),intra-block switching elements 42 (shown in FIGS. 3 and 4) and intra-rowswitching elements 44 (shown in FIG. 4).

As described below, the switching elements may include routingstructures and buffers. A STE 34, 36 can correspond to a state of a FSMimplemented by the FSM lattice 30. The STEs 34, 36 can be coupledtogether by using the configurable switching elements as describedbelow. Accordingly, a FSM can be implemented on the FSM lattice 30 byconfiguring the STEs 34, 36 to correspond to the functions of states andby selectively coupling together the STEs 34, 36 to correspond to thetransitions between states in the FSM.

FIG. 2 illustrates an overall view of an example of a FSM lattice 30.The FSM lattice 30 includes a plurality of blocks 32 that can beselectively coupled together with configurable inter-block switchingelements 40. The inter-block switching elements 40 may includeconductors 46 (e.g., wires, traces, etc.) and buffers 48, 50. In anexample, buffers 48 and 50 are included to control the connection andtiming of signals to/from the inter-block switching elements 40. Asdescribed further below, the buffers 48 may be provided to buffer databeing sent between blocks 32, while the buffers 50 may be provided tobuffer data being sent between inter-block switching elements 40.Additionally, the blocks 32 can be selectively coupled to an input block52 (e.g., a data input port) for receiving signals (e.g., data) andproviding the data to the blocks 32. The blocks 32 can also beselectively coupled to an output block 54 (e.g., an output port) forproviding signals from the blocks 32 to an external device (e.g.,another FSM lattice 30). The FSM lattice 30 can also include aprogramming interface 56 to configure (e.g., via an image, program) theFSM lattice 30. The image can configure (e.g., set) the state of theSTEs 34, 36. For example, the image can configure the STEs 34, 36 toreact in a certain way to a given input at the input block 52. Forexample, a STE 34, 36 can be set to output a high signal when thecharacter ‘a’ is received at the input block 52.

In an example, the input block 52, the output block 54, and/or theprogramming interface 56 can be implemented as registers such thatwriting to or reading from the registers provides data to or from therespective elements. Accordingly, bits from the image stored in theregisters corresponding to the programming interface 56 can be loaded onthe STEs 34, 36. Although FIG. 2 illustrates a certain number ofconductors (e.g., wire, trace) between a block 32, input block 52,output block 54, and an inter-block switching element 40, it should beunderstood that in other examples, fewer or more conductors may be used.

FIG. 3 illustrates an example of a block 32. A block 32 can include aplurality of rows 38 that can be selectively coupled together withconfigurable intra-block switching elements 42. Additionally, a row 38can be selectively coupled to another row 38 within another block 32with the inter-block switching elements 40. A row 38 includes aplurality of STEs 34, 36 organized into pairs of configurable elementsthat are referred to herein as groups of two (GOTs) 60. In an example, ablock 32 comprises sixteen (16) rows 38.

FIG. 4 illustrates an example of a row 38. A GOT 60 can be selectivelycoupled to other GOTs 60 and any other elements (e.g., a special purposeelement 58) within the row 38 by configurable intra-row switchingelements 44. A GOT 60 can also be coupled to other GOTs 60 in other rows38 with the intra-block switching element 42, or other GOTs 60 in otherblocks 32 with an inter-block switching element 40. In an example, a GOT60 has a first and second input 62, 64, and an output 66. The firstinput 62 is coupled to a first STE 34 of the GOT 60 and the second input64 is coupled to a second STE 36 of the GOT 60, as will be furtherillustrated with reference to FIG. 5.

In an example, the row 38 includes a first and second plurality of rowinterconnection conductors 68, 70. In an example, an input 62, 64 of aGOT 60 can be coupled to one or more row interconnection conductors 68,70, and an output 66 can be coupled to one or more row interconnectionconductor 68, 70. In an example, a first plurality of the rowinterconnection conductors 68 can be coupled to each STE 34, 36 of eachGOT 60 within the row 38. A second plurality of the row interconnectionconductors 70 can be coupled to only one STE 34, 36 of each GOT 60within the row 38, but cannot be coupled to the other STE 34, 36 of theGOT 60. In an example, a first half of the second plurality of rowinterconnection conductors 70 can couple to first half of the STEs 34,36 within a row 38 (one STE 34 from each GOT 60) and a second half ofthe second plurality of row interconnection conductors 70 can couple toa second half of the STEs 34, 36 within a row 38 (the other STE 34, 36from each GOT 60), as will be better illustrated with respect to FIG. 5.The limited connectivity between the second plurality of rowinterconnection conductors 70 and the STEs 34, 36 is referred to hereinas “parity”. In an example, the row 38 can also include a specialpurpose element 58 such as a counter, a configurable Boolean logicelement, look-up table, RAM, a field configurable gate array (FPGA), anapplication specific integrated circuit (ASIC), a configurable processor(e.g., a microprocessor), or other element for performing a specialpurpose function.

In an example, the special purpose element 58 comprises a counter (alsoreferred to herein as counter 58). In an example, the counter 58comprises a 12-bit configurable down counter. The 12-bit configurablecounter 58 has a counting input, a reset input, and zero-count output.The counting input, when asserted, decrements the value of the counter58 by one. The reset input, when asserted, causes the counter 58 to loadan initial value from an associated register. For the 12-bit counter 58,up to a 12-bit number can be loaded in as the initial value. When thevalue of the counter 58 is decremented to zero (0), the zero-countoutput is asserted. The counter 58 also has at least two modes, pulseand hold. When the counter 58 is set to pulse mode, the zero-countoutput is asserted when the counter 58 reaches zero. For example, thezero-count output is asserted during the processing of an immediatelysubsequent next data byte, which results in the counter 58 being offsetin time with respect to the input character cycle. After the nextcharacter cycle, the zero-count output is no longer asserted. In thismanner, for example, in the pulse mode, the zero-count output isasserted for one input character processing cycle. When the counter 58is set to hold mode the zero-count output is asserted during the clockcycle when the counter 58 decrements to zero, and stays asserted untilthe counter 58 is reset by the reset input being asserted.

In another example, the special purpose element 58 comprises Booleanlogic. For example, the Boolean logic may be used to perform logicalfunctions, such as AND, OR, NAND, NOR, Sum of Products (SoP),Negated-Output Sum of Products (NSoP), Negated-Output Product of Sume(NPoS), and Product of Sums (PoS) functions. This Boolean logic can beused to extract data from terminal state STEs (corresponding to terminalnodes of a FSM, as discussed later herein) in FSM lattice 30. The dataextracted can be used to provide state data to other FSM lattices 30and/or to provide configuring data used to reconfigure FSM lattice 30,or to reconfigure another FSM lattice 30.

FIG. 4A is an illustration of an example of a block 32 having rows 38which each include the special purpose element 58. For example, thespecial purpose elements 58 in the block 32 may include counter cells58A and Boolean logic cells 58B. While only the rows 38 in row positions0 through 4 are illustrated in FIG. 4A (e.g., labeled 38A through 38E),each block 32 may have any number of rows 38 (e.g., 16 rows 38), and oneor more special purpose elements 58 may be configured in each of therows 38. For example, in one embodiment, counter cells 58A may beconfigured in certain rows 38 (e.g., in row positions 0, 4, 8, and 12),while the Boolean logic cells 58B may be configured in the remaining ofthe 16 rows 38 (e.g., in row positions 1, 2, 3, 5, 6, 7, 9, 10, 11, 13,14, 15, and 16). The GOT 60 and the special purpose elements 58 may beselectively coupled (e.g., selectively connected) in each row 38 throughintra-row switching elements 44, where each row 38 of the block 32 maybe selectively coupled with any of the other rows 38 of the block 32through intra-block switching elements 42.

In some embodiments, each active GOT 60 in each row 38 may output asignal indicating whether one or more conditions are detected (e.g., asearch result is detected), and the special purpose element 58 in therow 38 may receive the GOT 60 output to determine whether certainquantifiers of the one or more conditions are met and/or count a numberof times a condition is detected. For example, quantifiers of a countoperation may include determining whether a condition was detected atleast a certain number of times, determining whether a condition wasdetected no more than a certain number of times, determining whether acondition was detected exactly a certain number of times, anddetermining whether a condition was detected within a certain range oftimes.

Outputs from the counter 58A and/or the Boolean logic cell 58B may becommunicated through the intra-row switching elements 44 and theintra-block switching elements 42 to perform counting or logic withgreater complexity. For example, counters 58A may be configured toimplement the quantifiers, such as asserting an output only when acondition is detected an exact number of times. Counters 58A in a block32 may also be used concurrently, thereby increasing the total bit countof the combined counters to count higher numbers of a detectedcondition. Furthermore, in some embodiments, different special purposeelements 58 such as counters 58A and Boolean logic cells 58B may be usedtogether. For example, an output of one or more Boolean logic cells 58Bmay be counted by one or more counters 58A in a block 32.

FIG. 5 illustrates an example of a GOT 60. The GOT 60 includes a firstSTE 34 and a second STE 36 coupled to intra-group circuitry 37. Forexample, the first STE 34 and a second STE 36 may have inputs 62, 64 andoutputs 72, 74 coupled to an OR gate 76 and a 3-to-1 multiplexer 78 ofthe intra-group circuitry 37. The 3-to-1 multiplexer 78 can be set tocouple the output 66 of the GOT 60 to either the first STE 34, thesecond STE 36, or the OR gate 76. The OR gate 76 can be used to coupletogether both outputs 72, 74 to form the common output 66 of the GOT 60.In an example, the first and second STE 34, 36 exhibit parity, asdiscussed above, where the input 62 of the first STE 34 can be coupledto some of the row interconnection conductors 68 and the input 64 of thesecond STE 36 can be coupled to other row interconnection conductors 70the common output 66 may be produced which may overcome parity problems.In an example, the two STEs 34, 36 within a GOT 60 can be cascadedand/or looped back to themselves by setting either or both of switchingelements 79. The STEs 34, 36 can be cascaded by coupling the output 72,74 of the STEs 34, 36 to the input 62, 64 of the other STE 34, 36. TheSTEs 34, 36 can be looped back to themselves by coupling the output 72,74 to their own input 62, 64. Accordingly, the output 72 of the firstSTE 34 can be coupled to neither, one, or both of the input 62 of thefirst STE 34 and the input 64 of the second STE 36. Additionally, aseach of the inputs 62, 64 may be coupled to a plurality of row routinglines, an OR gate may be utilized to select any of the inputs from theserow routing lines along inputs 62, 64, as well as the outputs 72, 74.

In an example, each state transition element 34, 36 comprises aplurality of memory cells 80, such as those often used in dynamic randomaccess memory (DRAM), coupled in parallel to a detect line 82. One suchmemory cell 80 comprises a memory cell that can be set to a data state,such as one that corresponds to either a high or a low value (e.g., a 1or 0). The output of the memory cell 80 is coupled to the detect line 82and the input to the memory cell 80 receives signals based on data onthe data stream line 84. In an example, an input at the input block 52is decoded to select one or more of the memory cells 80. The selectedmemory cell 80 provides its stored data state as an output onto thedetect line 82. For example, the data received at the input block 52 canbe provided to a decoder (not shown) and the decoder can select one ormore of the data stream lines 84. In an example, the decoder can convertan 8-bit ACSII character to the corresponding 1 of 256 data stream lines84.

A memory cell 80, therefore, outputs a high signal to the detect line 82when the memory cell 80 is set to a high value and the data on the datastream line 84 selects the memory cell 80. When the data on the datastream line 84 selects the memory cell 80 and the memory cell 80 is setto a low value, the memory cell 80 outputs a low signal to the detectline 82. The outputs from the memory cells 80 on the detect line 82 aresensed by a detection cell 86.

In an example, the signal on an input line 62, 64 sets the respectivedetection cell 86 to either an active or inactive state. When set to theinactive state, the detection cell 86 outputs a low signal on therespective output 72, 74 regardless of the signal on the respectivedetect line 82. When set to an active state, the detection cell 86outputs a high signal on the respective output line 72, 74 when a highsignal is detected from one of the memory cells 82 of the respective STE34, 36. When in the active state, the detection cell 86 outputs a lowsignal on the respective output line 72, 74 when the signals from all ofthe memory cells 82 of the respective STE 34, 36 are low.

In an example, an STE 34, 36 includes 256 memory cells 80 and eachmemory cell 80 is coupled to a different data stream line 84. Thus, anSTE 34, 36 can be programmed to output a high signal when a selected oneor more of the data stream lines 84 have a high signal thereon. Forexample, the STE 34 can have a first memory cell 80 (e.g., bit 0) sethigh and all other memory cells 80 (e.g., bits 1-255) set low. When therespective detection cell 86 is in the active state, the STE 34 outputsa high signal on the output 72 when the data stream line 84corresponding to bit 0 has a high signal thereon. In other examples, theSTE 34 can be set to output a high signal when one of multiple datastream lines 84 have a high signal thereon by setting the appropriatememory cells 80 to a high value.

In an example, a memory cell 80 can be set to a high or low value byreading bits from an associated register. Accordingly, the STEs 34 canbe configured by storing an image created by the compiler 20 into theregisters and loading the bits in the registers into associated memorycells 80. In an example, the image created by the compiler 20 includes abinary image of high and low (e.g., 1 and 0) bits. The image canconfigure the FSM lattice 30 to implement a FSM by cascading the STEs34, 36. For example, a first STE 34 can be set to an active state bysetting the detection cell 86 to the active state. The first STE 34 canbe set to output a high signal when the data stream line 84corresponding to bit 0 has a high signal thereon. The second STE 36 canbe initially set to an inactive state, but can be set to, when active,output a high signal when the data stream line 84 corresponding to bit 1has a high signal thereon. The first STE 34 and the second STE 36 can becascaded by setting the output 72 of the first STE 34 to couple to theinput 64 of the second STE 36. Thus, when a high signal is sensed on thedata stream line 84 corresponding to bit 0, the first STE 34 outputs ahigh signal on the output 72 and sets the detection cell 86 of thesecond STE 36 to an active state. When a high signal is sensed on thedata stream line 84 corresponding to bit 1, the second STE 36 outputs ahigh signal on the output 74 to activate another STE 36 or for outputfrom the FSM lattice 30.

In an example, a single FSM lattice 30 is implemented on a singlephysical device, however, in other examples two or more FSM lattices 30can be implemented on a single physical device (e.g., physical chip). Inan example, each FSM lattice 30 can include a distinct data input block52, a distinct output block 54, a distinct programming interface 56, anda distinct set of configurable elements. Moreover, each set ofconfigurable elements can react (e.g., output a high or low signal) todata at their corresponding data input block 52. For example, a firstset of configurable elements corresponding to a first FSM lattice 30 canreact to the data at a first data input block 52 corresponding to thefirst FSM lattice 30. A second set of configurable elementscorresponding to a second FSM lattice 30 can react to a second datainput block 52 corresponding to the second FSM lattice 30. Accordingly,each FSM lattice 30 includes a set of configurable elements, whereindifferent sets of configurable elements can react to different inputdata. Similarly, each FSM lattice 30, and each corresponding set ofconfigurable elements can provide a distinct output. In some examples,an output block 54 from a first FSM lattice 30 can be coupled to aninput block 52 of a second FSM lattice 30, such that input data for thesecond FSM lattice 30 can include the output data from the first FSMlattice 30 in a hierarchical arrangement of a series of FSM lattices 30.

In an example, an image for loading onto the FSM lattice 30 comprises aplurality of bits of data for configuring the configurable elements, theconfigurable switching elements, and the special purpose elements withinthe FSM lattice 30. In an example, the image can be loaded onto the FSMlattice 30 to configure the FSM lattice 30 to provide a desired outputbased on certain inputs. The output block 54 can provide outputs fromthe FSM lattice 30 based on the reaction of the configurable elements todata at the data input block 52. An output from the output block 54 caninclude a single bit indicating a search result of a given pattern, aword comprising a plurality of bits indicating search results andnon-search results to a plurality of patterns, and a state vectorcorresponding to the state of all or certain configurable elements at agiven moment. As described, a number of FSM lattices 30 may be includedin a state machine engine, such as state machine engine 14, to performdata analysis, such as pattern-recognition (e.g., speech recognition,image recognition, etc.) signal processing, imaging, computer vision,cryptography, and others.

FIG. 6 illustrates an example model of a finite state machine (FSM) thatcan be implemented by the FSM lattice 30. The FSM lattice 30 can beconfigured (e.g., programmed) as a physical implementation of a FSM. AFSM can be represented as a diagram 90, (e.g., directed graph,undirected graph, pseudograph), which contains one or more root nodes92. In addition to the root nodes 92, the FSM can be made up of severalstandard nodes 94 and terminal nodes 96 that are connected to the rootnodes 92 and other standard nodes 94 through one or more edges 98. Anode 92, 94, 96 corresponds to a state in the FSM. The edges 98correspond to the transitions between the states.

Each of the nodes 92, 94, 96 can be in either an active or an inactivestate. When in the inactive state, a node 92, 94, 96 does not react(e.g., respond) to input data. When in an active state, a node 92, 94,96 can react to input data. An upstream node 92, 94 can react to theinput data by activating a node 94, 96 that is downstream from the nodewhen the input data matches criteria specified by an edge 98 between theupstream node 92, 94 and the downstream node 94, 96. For example, afirst node 94 that specifies the character ‘b’ will activate a secondnode 94 connected to the first node 94 by an edge 98 when the first node94 is active and the character ‘b’ is received as input data. As usedherein, “upstream” refers to a relationship between one or more nodes,where a first node that is upstream of one or more other nodes (orupstream of itself in the case of a loop or feedback configuration)refers to the situation in which the first node can activate the one ormore other nodes (or can activate itself in the case of a loop).Similarly, “downstream” refers to a relationship where a first node thatis downstream of one or more other nodes (or downstream of itself in thecase of a loop) can be activated by the one or more other nodes (or canbe activated by itself in the case of a loop). Accordingly, the terms“upstream” and “downstream” are used herein to refer to relationshipsbetween one or more nodes, but these terms do not preclude the use ofloops or other non-linear paths among the nodes.

In the diagram 90, the root node 92 can be initially activated and canactivate downstream nodes 94 when the input data matches an edge 98 fromthe root node 92. Nodes 94 can activate nodes 96 when the input datamatches an edge 98 from the node 94. Nodes 94, 96 throughout the diagram90 can be activated in this manner as the input data is received. Aterminal node 96 corresponds to a search result of a sequence ofinterest in the input data. Accordingly, activation of a terminal node96 indicates that a sequence of interest has been received as the inputdata. In the context of the FSM lattice 30 implementing a patternrecognition function, arriving at a terminal node 96 can indicate that aspecific pattern of interest has been detected in the input data.

In an example, each root node 92, standard node 94, and terminal node 96can correspond to a configurable element in the FSM lattice 30. Eachedge 98 can correspond to connections between the configurable elements.Thus, a standard node 94 that transitions to (e.g., has an edge 98connecting to) another standard node 94 or a terminal node 96corresponds to a configurable element that transitions to (e.g.,provides an output to) another configurable element. In some examples,the root node 92 does not have a corresponding configurable element.

As will be appreciated, although the node 92 is described as a root nodeand nodes 96 are described as terminal nodes, there may not necessarilybe a particular “start” or root node and there may not necessarily be aparticular “end” or output node. In other words, any node may be astarting point and any node may provide output.

When the FSM lattice 30 is programmed, each of the configurable elementscan also be in either an active or inactive state. A given configurableelement, when inactive, does not react to the input data at acorresponding data input block 52. An active configurable element canreact to the input data at the data input block 52, and can activate adownstream configurable element when the input data matches the settingof the configurable element. When a configurable element corresponds toa terminal node 96, the configurable element can be coupled to theoutput block 54 to provide an indication of a search result to anexternal device.

An image loaded onto the FSM lattice 30 via the programming interface 56can configure the configurable elements and special purpose elements, aswell as the connections between the configurable elements and specialpurpose elements, such that a desired FSM is implemented through thesequential activation of nodes based on reactions to the data at thedata input block 52. In an example, a configurable element remainsactive for a single data cycle (e.g., a single character, a set ofcharacters, a single clock cycle) and then becomes inactive unlessre-activated by an upstream configurable element.

A terminal node 96 can be considered to store a compressed history ofpast search results. For example, the one or more patterns of input datarequired to reach a terminal node 96 can be represented by theactivation of that terminal node 96. In an example, the output providedby a terminal node 96 is binary, for example, the output indicateswhether a search result for a pattern of interest has been generated ornot. The ratio of terminal nodes 96 to standard nodes 94 in a diagram 90may be quite small. In other words, although there may be a highcomplexity in the FSM, the output of the FSM may be small by comparison.

In an example, the output of the FSM lattice 30 can comprise a statevector. The state vector comprises the state (e.g., activated or notactivated) of configurable elements of the FSM lattice 30. In anotherexample, the state vector can include the state of all or a subset ofthe configurable elements whether or not the configurable elementscorresponds to a terminal node 96. In an example, the state vectorincludes the states for the configurable elements corresponding toterminal nodes 96. Thus, the output can include a collection of theindications provided by all terminal nodes 96 of a diagram 90. The statevector can be represented as a word, where the binary indicationprovided by each terminal node 96 comprises one bit of the word. Thisencoding of the terminal nodes 96 can provide an effective indication ofthe detection state (e.g., whether and what sequences of interest havebeen detected) for the FSM lattice 30.

As mentioned above, the FSM lattice 30 can be programmed to implement apattern recognition function. For example, the FSM lattice 30 can beconfigured to recognize one or more data sequences (e.g., signatures,patterns) in the input data. When a data sequence of interest isrecognized by the FSM lattice 30, an indication of that recognition canbe provided at the output block 54. In an example, the patternrecognition can recognize a string of symbols (e.g., ASCII characters)to, for example, identify malware or other data in network data.

FIG. 7 illustrates an example of hierarchical structure 100, wherein twolevels of FSM lattices 30 are coupled in series and used to analyzedata. Specifically, in the illustrated embodiment, the hierarchicalstructure 100 includes a first FSM lattice 30A and a second FSM lattice30B arranged in series. Each FSM lattice 30 includes a respective datainput block 52 to receive data input, a programming interface block 56to receive configuring signals and an output block 54.

The first FSM lattice 30A is configured to receive input data, forexample, raw data at a data input block. The first FSM lattice 30Areacts to the input data as described above and provides an output at anoutput block. The output from the first FSM lattice 30A is sent to adata input block of the second FSM lattice 30B. The second FSM lattice30B can then react based on the output provided by the first FSM lattice30A and provide a corresponding output signal 102 of the hierarchicalstructure 100. This hierarchical coupling of two FSM lattices 30A and30B in series provides a means to provide data regarding past searchresults in a compressed word from a first FSM lattice 30A to a secondFSM lattice 30B. The data provided can effectively be a summary ofcomplex matches (e.g., sequences of interest) that were recorded by thefirst FSM lattice 30A.

FIG. 7A illustrates a second two-level hierarchy 100 of FSM lattices30A, 30B, 30C, and 30D, which allows the overall FSM 100 (inclusive ofall or some of FSM lattices 30A, 30B, 30C, and 30D) to perform twoindependent levels of analysis of the input data. The first level (e.g.,FSM lattice 30A, FSM lattice 30B, and/or FSM lattice 30C) analyzes thesame data stream, which includes data inputs to the overall FSM 100. Theoutputs of the first level (e.g., FSM lattice 30A, FSM lattice 30B,and/or FSM lattice 30C) become the inputs to the second level, (e.g.,FSM lattice 30D). FSM lattice 30D performs further analysis of thecombination the analysis already performed by the first level (e.g., FSMlattice 30A, FSM lattice 30B, and/or FSM lattice 30C). By connectingmultiple FSM lattices 30A, 30B, and 30C together, increased knowledgeabout the data stream input may be obtained by FSM lattice 30D.

The first level of the hierarchy (implemented by one or more of FSMlattice 30A, FSM lattice 30B, and FSM lattice 30C) can, for example,perform processing directly on a raw data stream. For example, a rawdata stream can be received at an input block 52 of the first level FSMlattices 30A, 30B, and/or 30C and the configurable elements of the firstlevel FSM lattices 30A, 30B, and/or 30C can react to the raw datastream. The second level (implemented by the FSM lattice 30D) of thehierarchy can process the output from the first level. For example, thesecond level FSM lattice 30D receives the output from an output block 54of the first level FSM lattices 30A, 30B, and/or 30C at an input block52 of the second level FSM lattice 30D and the configurable elements ofthe second level FSM lattice 30D can react to the output of the firstlevel FSM lattices 30A, 30B, and/or 30C. Accordingly, in this example,the second level FSM lattice 30D does not receive the raw data stream asan input, but rather receives the indications of search results forpatterns of interest that are generated from the raw data stream asdetermined by one or more of the first level FSM lattices 30A, 30B,and/or 30C. Thus, the second level FSM lattice 30D can implement a FSM100 that recognizes patterns in the output data stream from the one ormore of the first level FSM lattices 30A, 30B, and/or 30C. However, itshould also be appreciated that the second level FSM lattice 30D canadditionally receive the raw data stream as an input, for example, inconjunction with the indications of search results for patterns ofinterest that are generated from the raw data stream as determined byone or more of the first level FSM lattices 30A, 30B, and/or 30C. Itshould be appreciated that the second level FSM lattice 30D may receiveinputs from multiple other FSM lattices in addition to receiving outputfrom the one or more of the first level FSM lattices 30A, 30B, and/or30C. Likewise, the second level FSM lattice 30D may receive inputs fromother devices. The second level FSM lattice 30D may combine thesemultiple inputs to produce outputs. Finally, while only two levels ofFSM lattices 30A, 30B, 30C, and 30D are illustrated, it is envisionedthat additional levels of FSM lattices may be stacked such that thereare, for example, three, four, 10, 100, or more levels of FSM lattices.

FIG. 8 illustrates an example of a method 110 for a compiler to convertsource code into an image used to configure a FSM lattice, such aslattice 30, to implement a FSM. Method 110 includes parsing the sourcecode into a syntax tree (block 112), converting the syntax tree into anautomaton (block 114), optimizing the automaton (block 116), convertingthe automaton into a netlist (block 118), placing the netlist onhardware (block 120), routing the netlist (block 122), and publishingthe resulting image (block 124).

In an example, the compiler 20 includes an application programminginterface (API) that allows software developers to create images forimplementing FSMs on the FSM lattice 30. The compiler 20 providesmethods to convert an input set of regular expressions in the sourcecode into an image that is configured to configure the FSM lattice 30.The compiler 20 can be implemented by instructions for a computer havinga von Neumann architecture. These instructions can cause a processor 12on the computer to implement the functions of the compiler 20. Forexample, the instructions, when executed by the processor 12, can causethe processor 12 to perform actions as described in blocks 112, 114,116, 118, 120, 122, and 124 on source code that is accessible to theprocessor 12.

In an example, the source code describes search strings for identifyingpatterns of symbols within a group of symbols. To describe the searchstrings, the source code can include a plurality of regular expressions(regexes). A regex can be a string for describing a symbol searchpattern. Regexes are widely used in various computer domains, such asprogramming languages, text editors, network security, and others. In anexample, the regular expressions supported by the compiler includecriteria for the analysis of unstructured data. Unstructured data caninclude data that is free form and has no indexing applied to wordswithin the data. Words can include any combination of bytes, printableand non-printable, within the data. In an example, the compiler cansupport multiple different source code languages for implementing regexsincluding Perl, (e.g., Perl compatible regular expressions (PCRE)), PHP,Java, and .NET languages.

At block 112 the compiler 20 can parse the source code to form anarrangement of relationally connected operators, where different typesof operators correspond to different functions implemented by the sourcecode (e.g., different functions implemented by regexes in the sourcecode). Parsing source code can create a generic representation of thesource code. In an example, the generic representation comprises anencoded representation of the regexs in the source code in the form of atree graph known as a syntax tree. The examples described herein referto the arrangement as a syntax tree (also known as an “abstract syntaxtree”) in other examples, however, a concrete syntax tree as part of theabstract syntax tree, a concrete syntax tree in place of the abstractsyntax tree, or other arrangement can be used.

Since, as mentioned above, the compiler 20 can support multiplelanguages of source code, parsing converts the source code, regardlessof the language, into a non-language specific representation, e.g., asyntax tree. Thus, further processing (blocks 114, 116, 118, 120) by thecompiler 20 can work from a common input structure regardless of thelanguage of the source code.

As noted above, the syntax tree includes a plurality of operators thatare relationally connected. A syntax tree can include multiple differenttypes of operators. For example, different operators can correspond todifferent functions implemented by the regexes in the source code.

At block 114, the syntax tree is converted into an automaton. Anautomaton comprises a software model of a FSM which may, for example,comprise a plurality of states. In order to convert the syntax tree intoan automaton, the operators and relationships between the operators inthe syntax tree are converted into states with transitions between thestates. Moreover, in one embodiment, conversion of the automaton isaccomplished based on the hardware of the FSM lattice 30.

In an example, input symbols for the automaton include the symbols ofthe alphabet, the numerals 0-9, and other printable characters. In anexample, the input symbols are represented by the byte values 0 through255 inclusive. In an example, an automaton can be represented as adirected graph where the nodes of the graph correspond to the set ofstates. In an example, a transition from state p to state q on an inputsymbol α, i.e. δ(p, α), is shown by a directed connection from node p tonode q. In an example, a reversal of an automaton produces a newautomaton where each transition p→q on some symbol α is reversed q→p onthe same symbol. In a reversal, start states become final states and thefinal states become start states. In an example, the language recognized(e.g., matched) by an automaton is the set of all possible characterstrings which when input sequentially into the automaton will reach afinal state. Each string in the language recognized by the automatontraces a path from the start state to one or more final states.

At block 116, after the automaton is constructed, the automaton isoptimized to reduce its complexity and size, among other things. Theautomaton can be optimized by combining redundant states.

At block 118, the optimized automaton is converted into a netlist.Converting the automaton into a netlist maps each state of the automatonto a hardware element (e.g., STEs 34, 36, other elements) on the FSMlattice 30, and determines the connections between the hardwareelements.

At block 120, the netlist is placed to select a specific hardwareelement of the target device (e.g., STEs 34, 36, special purposeelements 58) corresponding to each node of the netlist. In an example,placing selects each specific hardware element based on general inputand output constraints for of the FSM lattice 30.

At block 122, the placed netlist is routed to determine the settings forthe configurable switching elements (e.g., inter-block switchingelements 40, intra-block switching elements 42, and intra-row switchingelements 44) in order to couple the selected hardware elements togetherto achieve the connections describe by the netlist. In an example, thesettings for the configurable switching elements are determined bydetermining specific conductors of the FSM lattice 30 that will be usedto connect the selected hardware elements, and the settings for theconfigurable switching elements. Routing can take into account morespecific limitations of the connections between the hardware elementsthan can be accounted for via the placement at block 120. Accordingly,routing may adjust the location of some of the hardware elements asdetermined by the global placement in order to make appropriateconnections given the actual limitations of the conductors on the FSMlattice 30.

Once the netlist is placed and routed, the placed and routed netlist canbe converted into a plurality of bits for configuring a FSM lattice 30.The plurality of bits are referred to herein as an image (e.g., binaryimage).

At block 124, an image is published by the compiler 20. The imagecomprises a plurality of bits for configuring specific hardware elementsof the FSM lattice 30. The bits can be loaded onto the FSM lattice 30 toconfigure the state of STEs 34, 36, the special purpose elements 58, andthe configurable switching elements such that the programmed FSM lattice30 implements a FSM having the functionality described by the sourcecode. Placement (block 120) and routing (block 122) can map specifichardware elements at specific locations in the FSM lattice 30 tospecific states in the automaton. Accordingly, the bits in the image canconfigure the specific hardware elements to implement the desiredfunction(s). In an example, the image can be published by saving themachine code to a computer readable medium. In another example, theimage can be published by displaying the image on a display device. Instill another example, the image can be published by sending the imageto another device, such as a configuring device for loading the imageonto the FSM lattice 30. In yet another example, the image can bepublished by loading the image onto a FSM lattice (e.g., the FSM lattice30).

In an example, an image can be loaded onto the FSM lattice 30 by eitherdirectly loading the bit values from the image to the STEs 34, 36 andother hardware elements or by loading the image into one or moreregisters and then writing the bit values from the registers to the STEs34, 36 and other hardware elements. In an example, the hardware elements(e.g., STEs 34, 36, special purpose elements 58, configurable switchingelements 40, 42, 44) of the FSM lattice 30 are memory mapped such that aconfiguring device and/or computer can load the image onto the FSMlattice 30 by writing the image to one or more memory addresses.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods can include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code can include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, the code may be tangibly stored on one ormore volatile or non-volatile computer-readable media during executionor at other times. These computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

Referring now to FIG. 9, an embodiment of the state machine engine 14(e.g., a single device on a single chip) is illustrated. As previouslydescribed, the state machine engine 14 is configured to receive datafrom a source, such as the memory 16 over a data bus. In the illustratedembodiment, data may be sent to the state machine engine 14 through abus interface, such as a double data rate (DDR) bus interface 130 (e.g.,a DDR3 or a DDR4 bus interface). The DDR bus interface 130 may becapable of exchanging (e.g., providing and receiving) data at a rategreater than or equal to 1 GByte/sec. Such a data exchange rate may begreater than a rate that data is analyzed by the state machine engine14. As will be appreciated, depending on the source of the data to beanalyzed, the bus interface 130 may be any suitable bus interface forexchanging data to and from a data source to the state machine engine14, such as a NAND Flash interface, peripheral component interconnect(PCI) interface, gigabit media independent interface (GMMI), etc. Aspreviously described, the state machine engine 14 includes one or moreFSM lattices 30 configured to analyze data. Each FSM lattice 30 may bedivided into two half-lattices. In the illustrated embodiment, each halflattice may include 24K STEs (e.g., STEs 34, 36), such that the lattice30 includes 48K STEs. The lattice 30 may comprise any desirable numberof STEs, arranged as previously described with regard to FIGS. 2-5.Further, while only one FSM lattice 30 is illustrated, the state machineengine 14 may include multiple FSM lattices 30, as previously described.

Data to be analyzed may be received at the bus interface 130 andprovided to the FSM lattice 30 through a number of buffers and bufferinterfaces. In the illustrated embodiment, the data path includes inputbuffers 132, an instruction buffer 133, process buffers 134, and aninter-rank (IR) bus and process buffer interface 136. The input buffers132 are configured to receive and temporarily store data to be analyzed.In one embodiment, there are two input buffers 132 (input buffer A andinput buffer B). Data may be stored in one of the two data input 132,while data is being emptied from the other input buffer 132, foranalysis by the FSM lattice 30. The bus interface 130 may be configuredto provide data to be analyzed to the input buffers 132 until the inputbuffers 132 are full. After the input buffers 132 are full, the businterface 130 may be configured to be free to be used for other purpose(e.g., to provide other data from a data stream until the input buffers132 are available to receive additional data to be analyzed). In theillustrated embodiment, the input buffers 132 may be 32 KBytes each. Theinstruction buffer 133 is configured to receive instructions from theprocessor 12 via the bus interface 130, such as instructions thatcorrespond to the data to be analyzed and instructions that correspondto configuring the state machine engine 14. The IR bus and processbuffer interface 136 may facilitate providing data to the process buffer134. The IR bus and process buffer interface 136 can be used to ensurethat data is processed by the FSM lattice 30 in order. The IR bus andprocess buffer interface 136 may coordinate the exchange of data, timingdata, packing instructions, etc. such that data is received and analyzedcorrectly. Generally, the IR bus and process buffer interface 136 allowsthe analyzing of multiple data sets in parallel through a logical rankof FSM lattices 30. For example, multiple physical devices (e.g., statemachine engines 14, chips, separate devices) may be arranged in a rankand may provide data to each other via the IR bus and process bufferinterface 136. For purposes of this application the term “rank” refersto a set of state machine engines 14 connected to the same chip select.In the illustrated embodiment, the IR bus and process buffer interface136 may include a 32 bit data bus. In other embodiments, the IR bus andprocess buffer interface 136 may include any suitable data bus, such asa 128 bit data bus.

In the illustrated embodiment, the state machine engine 14 also includesa de-compressor 138 and a compressor 140 to aid in providing statevector data through the state machine engine 14. The compressor 140 andde-compressor 138 work in conjunction such that the state vector datacan be compressed to minimize the data providing times. By compressingthe state vector data, the bus utilization time may be minimized. Thecompressor 140 and de-compressor 138 can also be configured to handlestate vector data of varying burst lengths. By padding compressed statevector data and including an indicator as to when each compressed regionends, the compressor 140 may improve the overall processing speedthrough the state machine engine 14. The compressor 140 may be used tocompress results data after analysis by the FSM lattice 30. Thecompressor 140 and de-compressor 138 may also be used to compress anddecompress configuration data. In one embodiment, the compressor 140 andde-compressor 138 may be disabled (e.g., turned off) such that dataflowing to and/or from the compressor 140 and de-compressor 138 is notmodified.

As previously described, an output of the FSM lattice 30 can comprise astate vector. The state vector comprises the state (e.g., activated ornot activated) of the STEs 34, 36 of the FSM lattice 30 and the dynamic(e.g., current) count of the counter 58. The state machine engine 14includes a state vector system 141 having a state vector cache memory142, a state vector memory buffer 144, a state vector intermediate inputbuffer 146, and a state vector intermediate output buffer 148. The statevector system 141 may be used to store multiple state vectors of the FSMlattice 30 and to provide a state vector to the FSM lattice 30 torestore the FSM lattice 30 to a state corresponding to the providedstate vector. For example, each state vector may be temporarily storedin the state vector cache memory 142. For example, the state of each STE34, 36 may be stored, such that the state may be restored and used infurther analysis at a later time, while freeing the STEs 34, 36 forfurther analysis of a new data set (e.g., search terms). Like a typicalcache, the state vector cache memory 142 allows storage of state vectorsfor quick retrieval and use, here by the FSM lattice 30, for instance.In the illustrated embodiment, the state vector cache memory 142 maystore up to 512 state vectors.

As will be appreciated, the state vector data may be exchanged betweendifferent state machine engines 14 (e.g., chips) in a rank. The statevector data may be exchanged between the different state machine engines14 for various purposes such as: to synchronize the state of the STEs34, 36 of the FSM lattices 30 of the state machine engines 14, toperform the same functions across multiple state machine engines 14, toreproduce results across multiple state machine engines 14, to cascaderesults across multiple state machine engines 14, to store a history ofstates of the STEs 34, 36 used to analyze data that is cascaded throughmultiple state machine engines 14, and so forth. Furthermore, it shouldbe noted that within a state machine engine 14, the state vector datamay be used to quickly configure the STEs 34, 36 of the FSM lattice 30.For example, the state vector data may be used to restore the state ofthe STEs 34, 36 to an initialized state (e.g., to prepare for a newinput data set), or to restore the state of the STEs 34, 36 to priorstate (e.g., to continue searching of an interrupted or “split” inputdata set). In certain embodiments, the state vector data may be providedto the bus interface 130 so that the state vector data may be providedto the processor 12 (e.g., for analysis of the state vector data,reconfiguring the state vector data to apply modifications,reconfiguring the state vector data to improve efficiency of the STEs34, 36, and so forth).

For example, in certain embodiments, the state machine engine 14 mayprovide cached state vector data (e.g., data stored by the state vectorsystem 141) from the FSM lattice 30 to an external device. The externaldevice may receive the state vector data, modify the state vector data,and provide the modified state vector data to the state machine engine14 for configuring the FSM lattice 30. Accordingly, the external devicemay modify the state vector data so that the state machine engine 14 mayskip states (e.g., jump around) as desired.

The state vector cache memory 142 may receive state vector data from anysuitable device. For example, the state vector cache memory 142 mayreceive a state vector from the FSM lattice 30, another FSM lattice 30(e.g., via the IR bus and process buffer interface 136), thede-compressor 138, and so forth. In the illustrated embodiment, thestate vector cache memory 142 may receive state vectors from otherdevices via the state vector memory buffer 144. Furthermore, the statevector cache memory 142 may provide state vector data to any suitabledevice. For example, the state vector cache memory 142 may provide statevector data to the state vector memory buffer 144, the state vectorintermediate input buffer 146, and the state vector intermediate outputbuffer 148.

Additional buffers, such as the state vector memory buffer 144, statevector intermediate input buffer 146, and state vector intermediateoutput buffer 148, may be utilized in conjunction with the state vectorcache memory 142 to accommodate rapid retrieval and storage of statevectors, while processing separate data sets with interleaved packetsthrough the state machine engine 14. In the illustrated embodiment, eachof the state vector memory buffer 144, the state vector intermediateinput buffer 146, and the state vector intermediate output buffer 148may be configured to temporarily store one state vector. The statevector memory buffer 144 may be used to receive state vector data fromany suitable device and to provide state vector data to any suitabledevice. For example, the state vector memory buffer 144 may be used toreceive a state vector from the FSM lattice 30, another FSM lattice 30(e.g., via the IR bus and process buffer interface 136), thede-compressor 138, and the state vector cache memory 142. As anotherexample, the state vector memory buffer 144 may be used to provide statevector data to the IR bus and process buffer interface 136 (e.g., forother FSM lattices 30), the compressor 140, and the state vector cachememory 142.

Likewise, the state vector intermediate input buffer 146 may be used toreceive state vector data from any suitable device and to provide statevector data to any suitable device. For example, the state vectorintermediate input buffer 146 may be used to receive a state vector froman FSM lattice 30 (e.g., via the IR bus and process buffer interface136), the de-compressor 138, and the state vector cache memory 142. Asanother example, the state vector intermediate input buffer 146 may beused to provide a state vector to the FSM lattice 30. Furthermore, thestate vector intermediate output buffer 148 may be used to receive astate vector from any suitable device and to provide a state vector toany suitable device. For example, the state vector intermediate outputbuffer 148 may be used to receive a state vector from the FSM lattice 30and the state vector cache memory 142. As another example, the statevector intermediate output buffer 148 may be used to provide a statevector to an FSM lattice 30 (e.g., via the IR bus and process bufferinterface 136) and the compressor 140.

Once a result of interest is produced by the FSM lattice 30, an eventvector may be stored in a event vector memory 150, whereby, for example,the event vector indicates at least one search result (e.g., detectionof a pattern of interest). The event vector can then be sent to an eventbuffer 152 for transmission over the bus interface 130 to the processor12, for example. As previously described, the results may be compressed.The event vector memory 150 may include two memory elements, memoryelement A and memory element B, each of which contains the resultsobtained by processing the input data in the corresponding input buffers132 (e.g., input buffer A and input buffer B). In one embodiment, eachof the memory elements may be DRAM memory elements or any other suitablestorage devices. In some embodiments, the memory elements may operate asinitial buffers to buffer the event vectors received from the FSMlattice 30, along results bus 151. For example, memory element A mayreceive event vectors, generated by processing the input data from inputbuffer A, along results bus 151 from the FSM lattice 30. Similarly,memory element B may receive event vectors, generated by processing theinput data from input buffer B, along results bus 151 from the FSMlattice 30.

In one embodiment, the event vectors provided to the event vector memory150 may indicate that a final result has been found by the FSM lattice30. For example, the event vectors may indicate that an entire patternhas been detected. Alternatively, the event vectors provided to theevent vector memory 150 may indicate, for example, that a particularstate of the FSM lattice 30 has been reached. For example, the eventvectors provided to the event vector memory 150 may indicate that onestate (i.e., one portion of a pattern search) has been reached, so thata next state may be initiated. In this way, the event vector memory 150may store a variety of types of results.

In some embodiments, IR bus and process buffer interface 136 may providedata to multiple FSM lattices 30 for analysis. This data may be timemultiplexed. For example, if there are eight FSM lattices 30, data foreach of the eight FSM lattices 30 may be provided to all of eight IR busand process buffer interfaces 136 that correspond to the eight FSMlattices 30. Each of the eight IR bus and process buffer interfaces 136may receive an entire data set to be analyzed. Each of the eight IR busand process buffer interfaces 136 may then select portions of the entiredata set relevant to the FSM lattice 30 associated with the respectiveIR bus and process buffer interface 136. This relevant data for each ofthe eight FSM lattices 30 may then be provided from the respective IRbus and process buffer interfaces 136 to the respective FSM lattice 30associated therewith.

The event vector memory 150 may operate to correlate each receivedresult with a data input that generated the result. To accomplish this,a respective result indicator may be stored corresponding to, and insome embodiments, in conjunction with, each event vector received fromthe results bus 151. In one embodiment, the result indicators may be asingle bit flag. In another embodiment, the result indicators may be amultiple bit flag. If the result indicators may include a multiple bitflag, the bit positions of the flag may indicate, for example, a countof the position of the input data stream that corresponds to the eventvector, the lattice that the event vectors correspond to, a position inset of event vectors, or other identifying information. These resultsindicators may include one or more bits that identify each particularevent vector and allow for proper grouping and transmission of eventvectors, for example, to compressor 140. Moreover, the ability toidentify particular event vectors by their respective results indicatorsmay allow for selective output of desired event vectors from the eventvector memory 150. For example, only particular event vectors generatedby the FSM lattice 30 may be selectively latched as an output. Theseresult indicators may allow for proper grouping and provision ofresults, for example, to compressor 140. Moreover, the ability toidentify particular event vectors by their respective result indicatorsallow for selective output of desired event vectors from the eventvector memory 150. Thus, only particular event vectors provided by theFSM lattice 30 may be selectively provided to compressor 140.

Additional registers and buffers may be provided in the state machineengine 14, as well. In one embodiment, for example, a buffer may storeinformation related to more than one process whereas a register maystore information related to a single process. For instance, the statemachine engine 14 may include control and status registers 154. Inaddition, a program buffer system (e.g., restore buffers 156) may beprovided for initializing the FSM lattice 30. For example, initial(e.g., starting) state vector data may be provided from the programbuffer system to the FSM lattice 30 (e.g., via the de-compressor 138).The de-compressor 138 may be used to decompress configuration data(e.g., state vector data, routing switch data, STE 34, 36 states,Boolean function data, counter data, match MUX data) provided to programthe FSM lattice 30.

Similarly, a repair map buffer system (e.g., save buffers 158) may alsobe provided for storage of data (e.g., save maps) for setup and usage.The data stored by the repair map buffer system may include data thatcorresponds to repaired hardware elements, such as data identifyingwhich STEs 34, 36 were repaired. The repair map buffer system mayreceive data via any suitable manner. For example, data may be providedfrom a “fuse map” memory, which provides the mapping of repairs done ona device during final manufacturing testing, to the save buffers 158. Asanother example, the repair map buffer system may include data used tomodify (e.g., customize) a standard programming file so that thestandard programming file may operate in a FSM lattice 30 with arepaired architecture (e.g., bad STEs 34, 36 in a FSM lattice 30 may bebypassed so they are not used). The compressor 140 may be used tocompress data provided to the save buffers 158 from the fuse map memory.As illustrated, the bus interface 130 may be used to provide data to therestore buffers 156 and to provide data from the save buffers 158. Aswill be appreciated, the data provided to the restore buffers 156 and/orprovided from the save buffers 158 may be compressed. In someembodiments, data is provided to the bus interface 130 and/or receivedfrom the bus interface 130 via a device external to the state machineengine 14 (e.g., the processor 12, the memory 16, the compiler 20, andso forth). The device external to the state machine engine 14 may beconfigured to receive data provided from the save buffers 158, to storethe data, to analyze the data, to modify the data, and/or to provide newor modified data to the restore buffers 156.

The state machine engine 14 includes a lattice programming andinstruction control system 159 used to configure (e.g., program) the FSMlattice 30 as well as provide inserted instructions, as will bedescribed in greater detail below. As illustrated, the latticeprogramming and instruction control system 159 may receive data (e.g.,configuration instructions) from the instruction buffer 133.Furthermore, the lattice programming and instruction control system 159may receive data (e.g., configuration data) from the restore buffers156. The lattice programming and instruction control system 159 may usethe configuration instructions and the configuration data to configurethe FSM lattice 30 (e.g., to configure routing switches, STEs 34, 36,Boolean logic cells, counters, match MUX) and may use the insertedinstructions to correct errors during the operation of the state machineengine 14. The lattice programming and instruction control system 159may also use the de-compressor 138 to de-compress data and thecompressor 140 to compress data (e.g., for data exchanged with therestore buffers 156 and the save buffers 158).

As discussed above, while the system 10 has been described as includingone state machine engine or automata processors (APs) 14, in otherembodiments, the system 10 may include a number of state machine enginesor automata processors (APs) 14. Referring to FIG. 10, a card, such as aPeripheral Component Interconnect express (PCIe) card 160, is provided.The PCIe card 160 includes 16 APs 14A-14P, for example. Alternatively,the PCIe card 160 may include a different number of APs 14A-14P, such as32. Each AP 14A-14P includes a respective lattice 30 (see e.g., FIG. 2).As previously described, each lattice 30 of each AP 14A-14P features acombination of logic including special purpose elements 58, such ascounters and Boolean logic cells, for instance, and a number of DRAMcells or RAM bits 80 on each die (i.e., AP). For instance, in oneembodiment, each STE 34, 36 includes 256 RAM bits 80. Thus, in oneembodiment, each PCIe card 160 includes 16 APs 14A-14P, each having arespective lattice 30. Each AP lattice 30 includes 192 blocks 32. Eachblock 32 includes 16 rows 38. Each row 38 includes 8 GOTs 60 and a SPE58. Each GOT 60 includes 2 STEs 34, 36. Each STE includes 256 RAM bits80. Alternatively, other numbers and groupings of the described elementsand structures may be employed on an AP 14A-14P. In addition toincluding the APs 14A-14P, the PCIe card 160 may include othercomponents, such as non-volatile memory (NVM) 162. The NVM 162 may beused to store card-specific and chip-specific information that may beused to identify the PCIe card 160 and to program the APs 14A-14P, asdiscussed below.

As previously described, in one embodiment, the 192 blocks 32 of each AP14A-14P are split into two half-lattices (HL0 and HL1), as illustratedin lattice 30 of FIG. 9. As will be appreciated, each half-lattice isgenerally divided into an x-y indexed grid of blocks 32, x ranging from0-7 and y ranging from 0-11. This indexed grid provides the total of 192blocks 32 (2 half-lattices*8 x-terms*12 y-terms=192). Each lattice 30may also be subdivided into 6 functionally identical regions (3 regionsper half-lattice). The regions may be identified as sets of 4 y-terms ona specific half-lattice, spanning all x-terms as follows:

HL Y Region 0 0-3 0 0 4-7 1 0 8-11 2 1 0-3 3 1 4-7 4 1 8-11 5

Functionally, these regions are all identical. However, the regions maybe useful when an AP 14A-14P produces a result of interest (e.g., amatch), and an event vector for the region(s) that produced the resultof interest may be created. As previously discussed, once a result ofinterest is produced by the FSM lattice 30, an event vector may bestored in the event vector memory 150 (FIG. 9), whereby, for example,the event vector indicates at least one search result (e.g., detectionof a pattern of interest). The event vector can then be sent to an eventbuffer 152 for transmission over the bus interface 130 to the processor12, for example.

As discussed previously, in order to utilize the highly parallel andconfigurable nature of the described state machines elements andinterconnections there between, each AP 14A-14P is selectivelyprogrammed by the compiler 20 to map the logical structure (e.g.,diagram 90) into a physical implementation to process and analyze thecomplex, unstructured data streams to produce a result of interest(e.g., detection of a pattern). Because of the unique nature of thelattices 30 of each AP 14A-14P, minor defects within a lattice of an AP14A-14P may render a single AP 14A-14P useless, unless a mechanism foreliminating the impact of such failures is provided. For instance,unlike typical DRAM arrays which may include large areas of redundantmemory cells for replacement of cells having failures detected therein,each lattice 30 of each AP 14A-14P has large sections of array that maybe only minimally repairable. A single defect in either RAM bits 80 orSPEs 58 can render a die (i.e., AP) useless. Thus, during testing andfabrication, a single error detected on an AP 14A-14P often results inthe entire AP 14A-14P being scrapped. As described in greater detailbelow, in accordance with embodiments of the invention, a solution forutilizing APs 14A-14P which may exhibit a certain number of defects isprovided.

As will be appreciated, each lattice 30 of each AP 14A-14P has a largenumber of repeated identical blocks 32 containing RAM bits 80 and SPEs58. Although row repairs in a block are possible, column repairs are notcompatible with the current test flow. Given that a single AP lattice 30on each AP 14A-14P is currently sub-divided into 6 regions and that eachof these regions may only include 16 redundant rows for instance, asingle column fail or scattered RAM bit fails numbering more than 16 onone STE 34, 36 could render an AP 14A-14P useless. Further, becausethere are no redundant instances of the logic elements (SPEs 58), if anyone of these logical elements is found to be defective, the entire AP14A-14P may be scrapped.

However, knowing that in any given use of an AP 14A-14P, a singleprogrammed AP 14A-14P may not use every RAM bit 80 and SPE 58 in aparticular block, and in fact will probably use only a minority of theseelements in a given block 32 based on how each block 32 of an AP 14A-14Pis programmed by the compiler 20, the programming of each AP 14A-14P canutilize AP-device-specific defect data to avoid non-reparable defectsdetected in each AP 14A-14P. In accordance with the describedembodiments, each of the APs 14A-14P may be tested and a device-specificdefect map may be generated such that each AP 14A-14P can be programmedutilizing the unique defect map to avoid non-repairable defectiveelements. For instance, and as described with reference to FIG. 11below, a minimum allowable number, type and locality of defects perblock or per region of the AP 14A-14P may be determined and for devicesexhibiting fewer failures than the pre-assigned threshold, a defect mapfor each AP 14A-14P may be generated during test flow to catalogueexactly what elements are defective. This part-specific defect data maythen be passed forward and stored in the NVM 162 on the PCIe board 160.In one embodiment, a device-specific defect map 164A-164P for eachrespective AP 14A-14P on the PCIe board 160 may be stored on the NVM162. A device-specific defect map 164A-164P stored on the NVM 162 of thePCIe card 160 can then be accessed by the compiler 20 to adaptivelyplace and route automata networks on and around partially failing blockswith little to no impact on end user functionality, while potentiallysignificantly increasing usable component yield by reducing the numberof scrapped chips. That is, when programming each respective AP 14A-14P,the compiler 20 can use the defect maps 164A-164P to selectively placeautomata networks in blocks 32 that can accommodate them. As long asmultiple blocks 32 do not share the same defects, system function as awhole will not be compromised. As used herein, “defects,” “logicdefects,” “logical defects,” and the like, refer to errors or defects inlogical elements, including special purpose elements 58, such ascounters and Boolean logic cells, for instance, and/or RAM bits 80 oneach the AP 14A-14P.

When a user defines an automata network to load onto an AP 14A-14P, theuser does not have direct control over how the automata network ismapped into the blocks 32. Instead, the compiler 20 is responsible forprogramming the automata network into each AP 14A-14P. As appreciated,during programming, an automata network uses one STE 34, 36 per node(e.g., 92, 94, 96) of the network, and may or may not use the SPEs 58(e.g., counter or Boolean logic cell). In a given configuration, eachSTE 34, 36 will likely rely on only a few of its associated 256 RAM bits80. Thus, utilizing the defect maps 164A-164P in conjunction with thelogical configuration of a given automata network when programming eachAP 14A-14P will allow the compiler 20 to determine which APs 14A-14P maybe utilized for a given network.

During programming, many automata networks will be loaded concurrentlyonto an AP 14A-14P. As noted, each network will only use a small subsetof the associated hardware components on the AP 14A-14P. If componentson the AP 14A-14P contain a small number of non-repeated failures thatare less than a predetermined threshold, the compiler 20 can beprogrammed to map the automata networks so that any blocks 32 withdefects are not paired with networks that require those failingelements. These blocks 32 with defects, however, can still be used forother automata networks that do not require the defective hardware.There are also multiple mappings for a single automata network. Atrivial example would be mirroring the network in the x or y direction,which would result in the same state machine function but differentnode-to-STE assignment. Put simply, the one-to-many nature of mapping astate machine diagram to the STE hardware allows for many possibledefect avoidance methods.

Turning now to FIG. 11, a method 170 for implementing the techniquesdescribed herein to seamlessly employ APs 14A-14P having an acceptablenumber, type and locality of defects below a predetermined threshold isdescribed. After fabrication, each AP 14 is tested for functionality, asindicated by element 172. As part of the functionality test, each of theRAM bits 80 in each STE 34, 36 is written to and read from to ensurethat the RAM bits 80 are functional. In addition to simply programmingeach RAM bit 80 during test, each potential symbol that may later bedetected may be used to test each RAM bit 80 to ensure that each RAM bit80 can detect each and every symbol that may later appear in a datastream to be searched. In addition, each of the SPEs 58 (counters andBoolean elements) is tested for functionality to ensure that theselogical elements are also functioning properly. That is, in addition totesting the other components of the AP 14, each of the RAM bits 80 andeach of the SPEs 58 are fully tested to ensure proper functionality, andany defects are documented. Previously, any detected defects in anyportion of the AP 14 would typically cause the entire AP 14 to bescrapped if such defects could not be easily corrected. However, inaccordance with the disclosed techniques, APs 14 having a certainnumber, type and locality of defects in the RAM bits 80 or SPEs 58 maybe utilized, thus increasing the number of usable chips and decreasingthe time, effort and money spent when usable part yields are low.

Referring again to FIG. 11, after testing an AP 14 (block 172), adetermination can be made as to whether the AP 14 has an acceptablenumber, type and locality of defects in the RAM bits 80 and the SPEs 58.That is, after testing the AP 14, a determination is made as to whetherthe number, type and location of defects in the RAM bits 80 and the SPEs58 is below a certain threshold level, as generally indicated bydecision block 174. Generally, the threshold is determined in such a wayas to provide customers with an assurance of some minimum degree offunctionality and flexibility of a given AP 14, such that the AP 14 issuitable for its intended purpose. Defining this minimum functionalityand thus the acceptable threshold level would be used to determine whatfailures are allowable at the component level, and how components (i.e.,APs 14) with different fail signatures (defects) can be paired. Incertain embodiments, it is also possible to create different productgrades for each AP 14—not based on speed, but on routing flexibility.Depending on the application, a customer may be able to utilize an AP 14with a lower product grade which could be obtained at a lower cost, forinstance. Advantageously, even in a top tier product grade, a small butnon-zero number of tolerable defects would greatly increase yield andnegligibly effect final system functionality.

To determine the threshold for acceptable failures for each RAM bit 80utilized in block 174, many different rules can be employed, dependingon the desired outcome and depending on the application utilizing the AP14. In one example, the threshold level of acceptable defects may be 1failing RAM bit 80 allowed per block 32. In a second example, thethreshold level of acceptable defects may be 8 failing RAM bits 80allowed per block 32, as long as no row 38 has more than one failing RAMbit 80. The second example may be useful for applications in which shortsearch sequences that can be fit into a single row 38 are employed, forinstance. In a third example, the threshold level of acceptable defectsmay be 8 failing RAM bits 80 allowed per block 32, as long as the typeof failures are all unique. For instance, keeping in mind that aparticular RAM bit 80 corresponds to a symbol (such as a letter) that anSTE 34, 36 can match (e.g., the letter “A”), if one STE 34, 36 cannotmatch the symbol (e.g., the letter “A”) but every other STE 34, 36 inthe block 32 can match they symbol, the AP 14 may still be acceptable.That is if 16 failures are detected in a block 32 but each of thefailures corresponds to a different symbol (e.g., letter), theprogramming flexibility of the block 32 may allow for such a thresholdlevel of defects. In a fourth example, the threshold level of acceptabledefects may be 16 failing RAM bits 80 allowed per block 32 as long as norow 38 has more than 1 failure and all failures are unique (i.e.,correspond to a different symbol).

These thresholds can also be determined considering an acceptable numberof defects in the SPEs 58, in addition to or independent of theacceptable number, location and type of defects in the RAM bits 80. Aswill be appreciated, the number of acceptable failures of the SPEs 58may be much lower than that of the RAM bits 80, due to the limitednumber of SPEs 58 on the AP 14. Accordingly, in one example, thethreshold level may be a single Boolean logic cell and/or a singlecounter on an AP 14. In another example, if a lattice 30 is divided intoregions, as discussed above, the threshold level may be a single Booleanlogic cell and/or a single counter per region of an AP 14. That latterexample may be acceptable considering that a region is a collection of32 blocks 32 and each block 32 contains 12 Boolean logic cells. Thus, 1defective Boolean cell per region means that approximately 99.7% of theBoolean logic cells remain functional in this example and that eachfailure would be separated from the others by a significant distance inthe lattice 30. This is much less impactful on potential programmingthan having the same number of logical element failures next to eachother in a single region, for instance. As will be appreciated, manydifferent rules can be utilized to determine the threshold for defining“good” chips that can be used with some acceptable number of defectsgreater than zero and “bad” chips that have an unacceptable number ofdefects and thus, may not be used (e.g., scrapped). However, once athreshold is determined prior to testing the AP (block 172), thisthreshold can be implemented to select the APs 14 that may be used(block 174).

If, during testing of the AP 14 (block 174), the AP 14 is determined tohave a number, type and location of defects above the predeterminedthreshold, the AP 14 may be scrapped, as indicated in block 176, orutilized in other ways than incorporation into a functional system(e.g., as a test part). If the AP 14 is determined to have an acceptablenumber, type and location of defects below the predetermined threshold,the AP 14 may be used in a functional system. In order to utilize theAPs 14 with acceptable defects below the threshold, a defect map foreach AP 14 is created, as generally indicated in block 178. As will beappreciated, the defect map may be created in any useful form such thata device-specific map is preserved and associated with each usable AP 14to indicate the location of such defects within the AP 14.

Subsequently, during fabrication of the PCIe card 160 having one or moreAPs 14A-14P, the functional APs 14A-14P having acceptable defects belowthe threshold may be electrically and physically coupled to thesubstrate of the PCIe card 160, as generally indicated by block 180. Aswill be appreciated, the substrate of the PCIe card 160 provides signalrouting lines and connections which will allow the PCIe card 160 havingfunctional APs 14A-14P to be incorporated into a system, such as thesystem 10. Also during fabrication of the PCIe card, the respectivedefect maps 164A-164P corresponding to each of the APs 14A-14P coupledto the PCIe card 160 are stored in the NVM 162 of the PCIe card 160, asgenerally indicated by block 182. By storing the device-specific defectmaps 164A-164P for each of the APs 14A-14P in the NVM 162, theoperational and completed PCIe card 160 may later be incorporated into asystem 10. The defect maps 164A-164P may be stored in any format useableby the compiler 20. For instance, an ordered list of numbers may beassigned to each RAM bit 80 and each SPE 34, 36 on an AP 14 to provide adefect location and type map, and a logical 0 or logical 1 may be usedto indicate whether each element passed or failed testing. The defectiveelements may then be “adaptively programmed” (i.e., programmed, usingthe defect map, only to detect symbols that were functionally detectableduring testing) or “mapped out” entirely during programming (i.e., thedefective elements of the AP 14 are not programmed for use).

As used herein, “mapping out,” or the like refers to not using certainelements in which a defect was detected. That is, if an element is saidto be “mapped out” during programming an AP 14 because a particular typeof defect was detecting during testing, for instance, this element isnot programmed for use, at all. For instance, if a defect is detectedduring testing in an SPE 58, such as a Boolean cell or counter, theseelements may not be programmed for use, at all (i.e., “mapped out”).Similarly, if multiple types defects in a single RAM bit 80 are detectedduring testing, these elements may not be programmed for use. Forinstance, if a particular RAM bit 80 is unable to detect multiplesymbols (e.g., the RAM bit 80 could not match multiple letters duringtesting), the RAM bit 80 may not be programmed for use.

In contrast, if an element is “adaptively programmed,” programming ofthe element is adapted based on the results of testing and theinformation contained in the defect map 164. Thus, during programming ofthe AP 14, the compiler utilizes the defect map 164 to ensure thatparticular elements are only used for to match symbols that have beensuccessfully matched during testing. Thus, if during testing aparticular RAM bit 80 cannot match one particular symbol (e.g., theletter “A”), but can match each of the other symbols required duringtesting, this particular RAM bit 80 may be adaptively programmed suchthat it is only utilized to detect a non-“A” symbol. Unlike RAM bits 80which are tested and determined to be fully functional, and thus may beprogrammed to detect any desired symbol, the programming of theparticular RAM bit 80 unable to detect the letter “A” is adapted suchthat it is programmed only to detect non-“A” symbols.

Once a PCIe card 160 is coupled into a system 10, the defect maps164A-164P can be accessed by the compiler 20 such that the defectiveelements of each AP 14A-14P can be mapped out or adaptively programmedduring programming of the APs 14A-14P such that defective elements areavoided for all or particular functionality during usage of the APs14A-14P, as generally indicated in block 184. Depending on the system,the defect maps 164A-164P may be accessed and employed by the compiler20 during one of the steps of method 110 (FIG. 8). For instance, invarious embodiments, the complier 20 may utilize the defect maps164A-164P while converting the syntax tree into an automaton (block114), optimizing the automaton (block 116), converting the automatoninto a netlist (block 118), or placing the netlist on hardware (block120), of process 110, as will be appreciated.

While the invention may be susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and have been described in detail herein.However, it should be understood that the invention is not intended tobe limited to the particular forms disclosed. Rather, the invention isto cover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention as defined by the followingappended claims.

What is claimed is:
 1. A system comprising: a memory configured to storea data stream; a state machine engine comprising a state machine latticeadaptively programmed based at least in part on a defect map comprisingan indication of logical defects, wherein the state machine engine isconfigured to produce a result of interest; and a processor configuredto exchange commands and data with the memory over a data bus andconfigured to: retrieve the data stream from the memory; instruct thestate machine engine to perform one or more data analysis operations onthe data stream; and receive the result of interest from the statemachine engine.
 2. The system of claim 1, wherein the state machinelattice comprises a Mealy architecture, a Moore architecture, one ormore Finite State Machines (FSMs), one or more Deterministic FSMs(DFSMs), one or more Bit-Parallel State Machines (BPSMs), or anycombination thereof.
 3. The system of claim 1, comprising a peripheralcomponent interconnect express (PCIe) board, wherein the state machineengine is coupled to the PCIe board.
 4. The system of claim 1, whereinthe state machine lattice comprises a plurality of state transitionelements (STEs), and wherein each of the STEs comprises a plurality ofmemory cells.
 5. The system of claim 1, wherein the state machinelattice comprises a plurality of special purpose elements.
 6. The systemof claim 5, wherein at least one of the plurality of special purposeelements comprises a Boolean logic cell, and wherein at least another ofthe plurality of special purpose elements comprises a counter.
 7. Thesystem of claim 1, wherein a portion of the state machine latticecomprises at least one non-repairable logical defect, and wherein theresult of interest is generated at least in part using the portion ofthe state machine lattice.
 8. The system of claim 1, wherein the statemachine lattice includes more than one non-repairable logical defectsbut less than a predetermined threshold of logical defects, and whereinthe defect map indicates a type and location of each of the more thanone non-repairable logical defects on the state machine lattice.
 9. Thesystem of claim 1, wherein the system comprises a compiler forconfiguring the state machine engine.
 10. The system of claim 9, whereinthe compiler is configured to access a non-volatile memory whenconfiguring the state machine engine and to utilize the defect map whenconfiguring the state machine engine in order to avoid configuring afirst subset of elements on the state machine engine having a number oflogical defects that exceeds a predetermined threshold indicated on thedefect map, wherein the non-volatile memory is configured to store thedefect map, and wherein the compiler is configured to adaptively programwithout avoiding a second subset of elements on the state machine enginebased on respective defects of the second subset of elements.
 11. Thesystem of claim 1, wherein the system comprises a plurality of statemachine engines each comprising a respective state machine lattice, andwherein a non-volatile memory has a respective defect map stored in thenon-volatile memory for each of the plurality of state machine engines.12. The system of claim 11, wherein each of the plurality of statemachine engines comprises a special purpose element comprising one of aBoolean logic cell or a counter.
 13. A system comprising: a plurality ofautomata processors each comprising a state machine lattice, whereineach of the plurality of automata processors is configured to workcollectively to determine a result of interest; a non-volatile memoryhaving a plurality of defect maps stored thereon, wherein each of theplurality of defect maps corresponds to a respective one of theplurality of automata processors; and a processor separate from theplurality of automata processors and configured to: transmit a datastream to the plurality of automata processors; receive the result ofinterest from the plurality of automata processors, wherein the resultof interest is generated based at least in part on a portion of arespective automata processor indicated as defective via a respectivedefect map of the plurality of defect maps; and perform one or moreoperations based at least in part on the result of interest.
 14. Thesystem of claim 13, wherein a respective state machine lattice comprisesa defect in a memory cell of the respective state machine lattice, andwherein the defect is indicated in one of the plurality of defect maps.15. The system of claim 13, wherein a respective state machine latticecomprises a defect in a special purpose element of the state machinelattice, and wherein the special purpose element comprises one of aBoolean logic cell or a counter, and wherein the defect is indicated inone of the plurality of defect maps.
 16. The system of claim 13, whereinthe system comprises a compiler for configuring each of the plurality ofautomata processors, wherein the compiler is configured to access eachof the plurality of defect maps when configuring each of the pluralityof automata processors, and wherein each of the plurality of defect mapscomprises information to facilitate the compiler avoiding configuringelements on the plurality of automata processors having a defectindicated on the plurality of defect maps.
 17. The system of claim 13,wherein the one or more operations comprise pattern recognitionoperations, and wherein the result of interest corresponds to arecognized pattern.
 18. A method, comprising: adaptively programming andmapping out elements of an automata processor based at least in part ona defect map configured to indicate at least one defect associated withthe automata processor; transmitting a data stream to the automataprocessor; receiving, at a processor, a processing result from theautomata processor; and performing the one or more operations, by theprocessor, based at least in part on the processing result.
 19. Themethod of claim 18, wherein performing the one or more operationscomprises applying the processing result as part of a patternrecognition operation.
 20. The method of claim 18, wherein the automataprocessor comprises a plurality of memory cells, wherein the defect mapis generated during a functional testing of the automata processor, andwherein generating the defect map comprises indicating defective memorycells of the automata processor.